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Record W4410572321 · doi:10.1002/pbc.31774

Contemporary Biology, Management, and Outcomes of Renal Medullary Carcinoma in Children and Adults: A Pediatric Surgical Oncology Research Collaborative Study

2025· article· en· W4410572321 on OpenAlexaff
Catherine B Beckhorn, Marcus M. Malek, Harold N. Lovvorn, Harold J. Leraas, Katlyn G. McKay, Nelly‐Ange T. Kontchou, Zachary J. Kastenberg, David Hoyt, Jonathan P. Roach, Emily K. Myers, Nicholas G. Cost, Bhargava Mullapudi, Charles R. Marchese, Amanda Jensen, Timothy B. Lautz, Michela Carter, Roshni Dasgupta, John Lundstedt, Joseph G. Brungardt, Lindsay J. Talbot, Andrew M. Davidoff, Andrew J. Murphy, Jennifer H. Aldrink, Sara A. Mansfield, Nelson Piché, Dave R. Lal, Brian T. Craig, Jennifer M Schuh, Barrett P. Cromeens, Sindhu V. Mannava, Shannon L. Castle, Adriana López, Kathryn Bandeira de Mello, Joshua Short, Robin T. Petroze, Shay Rajaval, Grace R. Thompson, Peter Mattei, David H. Rothstein, Elizabeth Fialkowski, Kathryn L. Fowler, Nathan Martchenke, Barrie S. Rich, Richard D. Glick, Erin G. Brown, Kathleen Doyle, Hannah Rinehardt, Natashia M. Seemann, Jacob B. Davidson, Claire A. Wilson, Hau D. Le, Devashish Joshi, Michael Stellon, Tamer A. Ahmed, Alexandra Dimmer, Erika A. Newman, Maya M. Hammoud, Keyonna M Williams, Christa N. Grant, Merit Gorgy, Stephanie F. Polites, Julia Debertin, Danielle B. Cameron, Alyssa Stetson, Eugene S. Kim, William G. Lee, Aaron Barkhordar, Mary T. Austin, Brian A. Coakley, Anastasia M Kahan, Joseph T. Murphy, Michael Pitonak, Chloé Boehmer, Elisabeth T. Tracy

Bibliographic record

VenuePediatric Blood & Cancer · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsLondon Health Sciences CentreUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineNephrectomyRenal cell carcinomaSickle cell traitInternal medicineOncologyRetrospective cohort studyProportional hazards modelDiseaseSurgeryKidney

Abstract

fetched live from OpenAlex

BACKGROUND: Renal medullary carcinoma (RMC) is an aggressive tumor associated with sickle cell trait. Despite treatment advances for other rare renal tumors, RMC survival remains poor. We aimed to describe the contemporary management and survival of children and adults with RMC. PROCEDURE: In this multicenter retrospective cohort study, Pediatric Surgical Oncology Research Collaborative sites searched their databases for patients diagnosed with RMC (2000-2022). Descriptive statistics were calculated and survival analyses performed using Kaplan-Meier and Cox regression. RESULTS: Thirty-four patients with RMC were identified. Median age was 19 years (IQR: 15-28; range: 7-52). Most were male (24/34; 71%), Black (27/32; 84%), had sickle cell trait or disease (30/33; 91%), presented with metastatic disease (27/34; 79%), and were symptomatic at presentation (32/34; 94%). Median overall survival (OS) was 24 months from diagnosis (16 months for children, 28 months for adults, p = 0.6). Receipt of platinum-based chemotherapy (23/34; 68%) was associated with significantly higher OS than other regimens (35 vs. 5 months, p < 0.001). Nephrectomy (24/34; 71%) was associated with significantly improved OS compared with non-operative management (34 vs. 7 months, p = 0.001). Immunotherapy, targeted therapy, or radiation therapy were not associated with significant differences in OS, nor were age, sex, race, sickle cell status, SMARCB1/INI-1, stage, nephrectomy approach, retroperitoneal lymph node dissection, gross residual disease, margins, or tumor size. CONCLUSIONS: RMC survival remains poor despite newer therapies. Nephrectomy and platinum-based chemotherapy should be considered in locally advanced and metastatic disease. Coordinated international cooperative group studies are needed to meaningfully improve RMC survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.360
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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